
Machine and Deep Learning Approaches for Cancer Drug Repurposing
In past years, medical oncology has witnessed an unprecedented explosion in the understanding of cancer pathophysiology and pathogenesis. With the advancement of next-generation sequencing technologies such as single-cell RNA sequencing, we are better equipped to explore and model complex phenomena such as cancer heterogeneity, resistance, and etiologies at a granular level. Read more “Machine and Deep Learning Approaches for Cancer Drug Repurposing”